For many image analysis applications it is necessary to identify the boundary of features shown in an image. Particularly in medical imaging, such as X-ray images of lungs or breasts for diagnosing cancerous tumors and the like, it is essential to identify the boundary of the lung lobe or breast tissue.
More than 10% of women in the western world contract breast cancer, and the success and ease of treatment is highly dependent on early diagnosis. Mammography is the use of low-dose x-ray radiation to image the tissue inside the breast. The technique is used to screen for and diagnose breast cancer by detecting tumors or other changes in breast tissue and aids in early detection of malignant tumors, which improves chances of successful treatment. It can identify abnormalities before a lump can be felt and provides the only reliable method of locating abnormal growths in the milk ducts. Thus it may facilitate locating suspected tumors, prior to a biopsy or surgery.
In consequence of the dangers of breast cancer and the success of mammography, the guidelines laid by the U.S. Department of Health and Human Services (HHS), the American Cancer Society (ACS), the American Medical Association (AMA) and the American College of Radiology (ACR) recommend that screening mammograms be performed annually for all women over the age of 40 in good health, with annual mammograms being advisable at earlier ages for women with a family history of breast cancer or having had prior breast biopsies.
In mammography, the breast is compressed between two plates and exposed to X-rays. Two pictures of each breast are generally taken during a screening mammogram, with extra images from different angles being sometimes necessary for women with breast implants. With so many scans requiring analysis, it is essential to automate the analysis as much as possible and to optimize the examination of the medical images, both by increased accuracy of the analysis and by faster processing times.
It is very important to detect the outer boundary of the breast to ensure that analysis of suspicious micro-features only occurs in genuine breast tissue and not in other parts of the chest, and that the whole of the breast tissue is included in the analysis. An exact definition of the breast area, generally known as breast segmentation, is required for optimal usage of the X-ray images obtained by digital mammography, in particular for soft display on a monitor and for computer aided diagnosis (CAD). Accurate breast segmentation allows for analyzing all the breast tissue while minimizing processing time and avoiding the generation of false marks outside the breast. In particular, it will be noted that accurate breast segmentation allows for optimally enlarging the image without cutting out any breast tissue.
Breast segmentation is not trivial however. Since the size of the breast is highly variable between women, the thickness of the imaged compressed tissues differs significantly between subjects. The tissue composition of the breast is also highly variable and therefore the average absorption of X-rays by the breast tissue varies significantly between women.
Since the density, size and texture of breasts are so very variable, determination of the boundary of the breast in x-ray images is not easy and current algorithms are time consuming and expensive in terms of the computer power required.
Digital mammography is preferably to conventional film in that better contrast is available. Digital mammogram images are stored as digital pictures which can be transmitted easily for remote consultation.
Compared to other anatomical regions, the breast has very low physical contrast because it is composed completely of soft tissues. In general, the breast consists of a background of fat surrounding the slightly denser, glandular structures and pathologic tissues or cysts if they are present. Typical breast calcifications are very small and thin and produce low physical contrast despite calcium being somewhat denser and having a higher atomic number than the elements of soft tissues.
Mammography is generally performed with a spectrum containing photons within a relatively narrow energy range (19 keV-21 kev) in an attempt to obtain high contrast with minimal dosage. The spectrum is produced using the characteristic radiation from a molybdenum anode x-ray tube and filtered by either a molybdenum or a rhodium filter.
The molybdenum anode, molybdenum filter system is quite good for general mammography in that it provides a spectrum that is very close to the optimum spectrum for smaller and less dense breasts. Many mammography machines give the operator the opportunity of selecting between molybdenum and rhodium filters, the latter being useful when imaging denser breasts.
Some systems have dual track anodes so that either molybdenum or rhodium can be selected as the anode material. Because of its higher atomic number (Z) rhodium produces characteristic x-radiation with higher energies than molybdenum. When the rhodium anode is selected, the beam penetration is increased. Generally, this produces better results when imaging dense breast. Since the physical parameters of X-ray sources used for mammography vary between different systems, a high variability is introduced between mammography images which is an artifact of the imaging parameters and not a result of different physiologies.
Although the magnification, brightness, contrast and orientation can be altered in digital X-ray images to display the breast tissue more clearly, such image enhancement techniques are required to be extensively automated by simple procedures to enable fast and accurate diagnosis.
The problem of breast segmentation has been extensively addressed in the prior art, and various methods have been proposed. Current methods are based mainly on grey level thresholding as described by Bick et al. in U.S. Pat. No. 5,452,367). Sometimes this technique is used in conjunction with local gradient methods such as described in U.S. Pat. No. 5,572,565 to Abdel-Mottaleb, for example.
U.S. Pat. No. 5,825,910 to Vafai describes the use of local gradient methods as a refinement after coarse breast segmentation. Such methods make it very difficult to precisely include all tissue, especially the peripheral skin or other border areas having low tissue density.
There is thus a need for methods of image analysis for extracting the boundary of features, particularly for medical image analysis and most particularly for the demanding and critical application of breast segmentation for mammography, and the present invention addresses this need.